Statistical Fraud Detection: A Review

Bolton, Richard J.; Hand, David J.

doi:10.1214/ss/1042727940

Cited by 883 publications

(541 citation statements)

References 73 publications

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“…Second, a parallel version of DBSCAN [16], which splits the dataset on multiple machine. Fraud detection, mainly focused on credit card fraud, is a wide research topic, for which we refer the reader to [5,14,4].…”

Section: Discussionmentioning

confidence: 99%

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BankSealer: An Online Banking Fraud Analysis and Decision Support System

Carminati

Caron

Maggi

et al. 2014

IFIP Advances in Information and Communication Technology

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We propose a semi-supervised online banking fraud analysis and decision support approach. During a training phase, it builds a profile for each customer based on past transactions. At runtime, it supports the analyst by ranking unforeseen transactions that deviate from the learned profiles. It uses methods whose output has a immediate statistical meaning that provide the analyst with an easy-to-understand model of each customer's spending habits. First, we quantify the anomaly of each transaction with respect to the customer historical profile. Second, we find global clusters of customers with similar spending habits. Third, we use a temporal threshold system that measures the anomaly of the current spending pattern of each customer, with respect to his or her past spending behavior. As a result, we mitigate the undertraining due to the lack of historical data for building of well-trained profiles (of fresh users), and the users that change their (spending) habits over time. Our evaluation on real-world data shows that our approach correctly ranks complex frauds as "top priority".

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Section: Discussionmentioning

confidence: 99%

“…The popularity of Internet banking has led to an increase of frauds, resulting in substantial financial losses [15,4]. Banking frauds increased 93% in 2009-2010 [6], and 30% in 2012-2013 [8].…”

Section: Introductionmentioning

confidence: 99%

BankSealer: An Online Banking Fraud Analysis and Decision Support System

Carminati

Caron

Maggi

et al. 2014

IFIP Advances in Information and Communication Technology

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“…This distinguishes it from statistical approaches to assess fraud risk [12], which use patterns of past client behavior to assess fraud risks in new business models. Since the new business model has by definition not contributed to the statistical data on which this assessment is based, statistics-based fraud assessment leaves one with unknown and un-estimated risks.…”

Section: Introductionmentioning

confidence: 99%

Automated Identification and Prioritization of Business Risks in e-service Networks

Wieringa

Gordijn

2016

Lecture Notes in Business Information Processing

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Abstract. Modern e-service providers rely on service innovation to stay relevant. Once a new service package is designed, implementation-specific aspects such as value (co-)creation and cost/benefit analysis are investigated. However, due to time-to-market or competitive advantage constraints, innovative services are rarely assessed for potential risks of fraud before they are put out on the market. But these risks may result in loss of economic value for actors involved in the e-service's provision. Our e 3 fraud approach automatically generates and prioritizes undesiredable scenarios from a business value model of the e-service, thereby drastically reducing the time needed to conduct an assessment. We provide examples from telecom service provision to motivate and illustrate the utility of the tool.

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“…Fuentes gubernamentales de los Estados Unidos han señalado que 10% del gasto total en salud contiene elementos de fraude (2,12), en España la sobreutilización del seguro de enfermedad podría llegar a 14% (7) y en Chile las estimaciones ascienden a 27% (13).…”

Section: Introductionunclassified

“…Hasta el momento no se han dado a conocer explícitamente detalles de los modelos y métodos de detección empleados, ya que esa información podría ayudar a evadir la detección (12).…”

Section: Introductionunclassified

Fraudes a los sistemas de salud en Chile: un modelo para su detección

Mesa¹,

Raineri²,

Maturana³

et al. 2009

Rev Panam Salud Publica

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Objectives. To develop a model for detecting cases of organized fraud in Chile based on data from the legal forms for medically authorized leave (formulario legal de licencia médica curativa-MAL) and to establish the relevance of this data to fraud detection. Methods.A binomial logistic regression model was employed using four variables from the MAL form, a national requirement for illness-related work absences: the number of legal absences taken by a single person, the number of days authorized by the prescribing doctor, the total cost per illness, and a dichotic variable reflecting whether or not the diagnosis is one that can be proven. The analysis involved 4 079 MAL forms that had been submitted in 2003 to a private health provider and of which 356 were already identified as fraudulent by a panel of medical fraud experts. Results. The model successfully identified 99.71% of the fraudulent medical authorizations and 99.86% of the non-fraudulent, according to the criteria of the panel of fraud experts. Three of the variables employed had statistically-significant independent predictive power. The positive predictive value of the proposed model was 98.59%, while its negative predictive value was 99.97%. Conclusions. The binomial logistic model that was developed uses four variables that are common to all MAL forms in use by Chile's public as well as private insurers, permitting separation of fraudulent from non-fraudulent requests to be more accurate, more timely, and at a cost lower that of an expert panel.Fraud; insurance, health; decision support techniques, Chile. ABSTRACTFraud in the health systems of Chile: a detection model

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Statistical Fraud Detection: A Review

Cited by 883 publications

References 73 publications

BankSealer: An Online Banking Fraud Analysis and Decision Support System

BankSealer: An Online Banking Fraud Analysis and Decision Support System

Automated Identification and Prioritization of Business Risks in e-service Networks

Fraudes a los sistemas de salud en Chile: un modelo para su detección

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